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distilbert_sa_GLUE_Experiment_logit_kd_stsb
This model is a fine-tuned version of distilbert-base-uncased on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 1.1792
- Pearson: 0.1721
- Spearmanr: 0.1790
- Combined Score: 0.1755
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
---|---|---|---|---|---|---|
1.6404 | 1.0 | 23 | 1.2916 | 0.0486 | 0.0549 | 0.0518 |
1.0137 | 2.0 | 46 | 1.6141 | 0.0993 | 0.0887 | 0.0940 |
0.9483 | 3.0 | 69 | 1.1792 | 0.1721 | 0.1790 | 0.1755 |
0.8128 | 4.0 | 92 | 1.3857 | 0.1405 | 0.1428 | 0.1416 |
0.6939 | 5.0 | 115 | 1.2921 | 0.1809 | 0.1954 | 0.1881 |
0.5773 | 6.0 | 138 | 1.4230 | 0.1545 | 0.1669 | 0.1607 |
0.5082 | 7.0 | 161 | 1.4663 | 0.1550 | 0.1645 | 0.1598 |
0.4467 | 8.0 | 184 | 1.4837 | 0.1520 | 0.1603 | 0.1561 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2